ReviewGigaScience2014
The rise of large-scale imaging studies in psychiatry.
Review in GigaScience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Large language models can extract metadata for annotation of human neuroimaging publications.Frontiers in neuroinformatics · 2025Article
- Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Subcortical surface morphometry in substance dependence: An ENIGMA addiction working group study.Addiction biology · 2020Article
- Understanding the Complexity of Teacher Emotions From Online Forums: A Computational Text Analysis Approach.Frontiers in psychology · 2020Article
- A comparison of manual tracing and FreeSurfer for estimating hippocampal volume over the adult lifespan.Human brain mapping · 2018Article
- Harmonization of multi-site diffusion tensor imaging data.NeuroImage · 2017Article
- Orbitofrontal and caudate volumes in cannabis users: a multi-site mega-analysis comparing dependent versus non-dependent users.Psychopharmacology · 2017Article
- Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.NeuroImage · 2017Review
- Auditory Hallucinations and the Brain's Resting-State Networks: Findings and Methodological Observations.Schizophrenia bulletin · 2016Review
- Biomarkers in Psychiatry - A Critique.Annals of neurosciences · 2016Article
- A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets.Frontiers in neuroinformatics · 2016Article
- SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration.NeuroImage · 2016Article
- Terminology development towards harmonizing multiple clinical neuroimaging research repositories.Data integration in the life sciences : ... International Workshop, DILS ... : proceedings. DILS (Conference) · 2015Article
- SchizConnect: Virtual Data Integration in Neuroimaging.Data integration in the life sciences : ... International Workshop, DILS ... : proceedings. DILS (Conference) · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
From the initial arguments over whether 12 to 20 subjects were sufficient for an fMRI study, sample sizes in psychiatric neuroimaging studies have expanded into the tens of thousands. These large-scale imaging studies fall into several categories, each of which has specific advantages and challenges. The different study types can be grouped based on their level of control: meta-analyses, at one extreme of the spectrum, control nothing about the imaging protocol or subject selection criteria in the datasets they include, On the other hand, planned multi-site mega studies pour intense efforts into strictly having the same protocols. However, there are several other combinations possible, each of which is best used to address certain questions. The growing investment of all these studies is delivering on the promises of neuroimaging for psychiatry, and holds incredible potential for impact at the level of the individual patient. However, to realize this potential requires both standardized data-sharing efforts, so that there is more staying power in the datasets for re-use and new applications, as well as training the next generation of neuropsychiatric researchers in "Big Data" techniques in addition to traditional experimental methods. The increased access to thousands of datasets along with the needed informatics demands a new emphasis on integrative scientific methods.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.